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Realistic synthetic medication data.
A novel method to create realistic synthetic medication data
AbstractObjective. Synthea is a synthetic patient generator that creates synthetic medical records, including medication profiles. Prior to our work, Synthea pr
A team of six pharmacists won the ONC Synthetic Health Data Challenge in 2021 with their submission to improve the diversity of medication data in the synthetic health data generator Synthea™ (created by MITRE).
Why did we rename it?
For the contest, we named our software Medication Diversification Tool (MDT). While MDT is easy to say, the whole name is a mouthful and doesn’t really align with the names of the other things CodeRx is working on. We forked the MDT repo and called the fork ForgeRx in a nod to forging (i.e. faking) synthetic data, but also the act of creating something in a forge (i.e. blacksmith’s forge) out of raw materials.
There is a roadmap of improvements we would still like to make to ForgeRx, but honestly we have not done much yet as our focus has been on other projects. If you would like to contribute, please contact us or join our Slack!
Oh, and we published in JAMIA Open!
👇 Historical information about Medication Diversification Tool (MDT) below:
🏆 First place winners of the ONC Synthetic Health Data Challenge
Team CodeRx for the Medication Diversification Tool (MDT) submission to the Synthetic Health Data Challenge consists of Joseph LeGrand (team leader), Kent Bridgeman, Kristen Tokunaga, Robert Hodges, Dalton Fabian, and Yevgeny (Eugene) Bulochnik.
Medication Diversification Tool (MDT) links:
- Winning solutions webinar - START HERE
- Final presentation video
- Final presentation paper
- CodeRx MDT GitHub repo
- Final presentation Miro board
Challenge winner announcements:
Synthea™ generates open source synthetic patient records from birth to death. The problem we are trying to solve is how to make the medication selection in these populations more diverse and realistic. For instance, 100% of patients who have hypothyroidism are prescribed the same exact strength of generic levothyroxine. We hope to show how we can use open source tools and data to increase the diversity of medication selection in this population and to better represent a realistic patient population in the US.
Download the tip sheet below to create your own realistic pediatric asthma medication data using CodeRx MDT and Synthea™.
Current state versus with the medication diversification tool for a hypothyroidism use case.
About the challenge
The U.S. Department of Health and Human Services’ (HHS) Office of the National Coordinator for Health Information Technology (ONC) today announced $100,000 in total awards to six winners of the Synthetic Health Data Challenge (Challenge). Synthetic health data (i.e., data that is artificially created to mimic real-world data), is important to researchers, health IT developers, and informaticians, among others, who need data to test new ideas until access to secure and actual clinical data is available.
The Challenge was conducted under ONC’s Synthetic Health Data Generation to Accelerate Patient-Centered Outcomes Research (PCOR) project, which is supported by HHS’ Office of the Secretary Patient-Centered Outcomes Research Trust Fund. Challenge winners created and tested innovative and novel solutions that will further augment the capabilities of Synthea™, an open-source synthetic health data generator that models the medical histories of synthetic patients. The availability of reliable and robust synthetic data generation tools safeguard patient privacy because they support appropriate stewardship practices in which real patient data is only accessed and used when necessary.
“The availability of realistic, synthetic data is a vital part of supporting iterative testing models and early stage research and product development,” said Steve Posnack, deputy national coordinator for health information technology. “We received a lot of inspired submissions that took this work to the next level and hope that each winner can serve as a foundation to further enhance tools that create synthetic data.”